针对自动驾驶三维建图中存在的建图不准确以及位姿飘移的问题，利用激光雷达里程计消除惯性测量单元（Inertial Measurement Unit， nIMU）累计误差并通过IMU预积分去除激光雷达点云畸变，形成激光雷达与IMU的紧耦合建图系统；通过增加回环检测因子、激光雷达里程计因子以及IMU预积分因子进行后端图优化，旨在提升定位建图的全局一致性，减小位姿估计误差，降低累计漂移误差。最后，在学校园区实地场景以及利用开源数据集KITTI进行实验验证，实验表明，在选取的学校园区实地场景下，改进算法APE误差均值相较于原算法降低了11.04%，APE均方根误差较于原算法降低了17.35%；改进算法在KITTI数据集场景下平均APE误差下降了10.04%，均方误差方面相较于原算法平均下降了12.04%。研究结果表明，改进的建图方法能够有效提高建图的位姿估计精度与地图构建精度。

Optical networks are evolving toward ultrawide bandwidth and autonomous operation. In this scenario, it is crucial to accurately model and control optical power evolutions (OPEs) through optical amplifiers (OAs), as they directly affect the signal-to-noise ratio and fiber nonlinearities. However, a fundamental contradiction arises between the complex physical phenomena in optical transmission and the required precision in network control. Traditional theoretical methods underperform due to ideal assumptions, while data-driven approaches entail exorbitant costs associated with acquiring massive amounts of data to achieve the desired level of accuracy. In this work, we propose a Bayesian inference framework (BIF) to construct the digital twin of OAs and control OPE in a data-efficient manner. Only the informative data are collected to balance the exploration and exploitation of the data space, thus enabling efficient autonomous-driving optical networks (ADONs). Simulations and experiments demonstrate that the BIF can reduce the data size for modeling erbium-doped fiber amplifiers by 80% and Raman amplifiers by 60%. Within 30 iterations, the optimal controlling performance can be achieved to realize target signal/gain profiles in links with different types of OAs. The results show that the BIF paves the way to accurately model and control OPE for future ADONs.

提出利用P型开关作为充电管、N型开关作为放电管串联形成半桥结构，将其门极也短接，以同一个信号同时驱动充电管和放电管。再利用共原边的串心磁环驱动方案，只需一个半桥电路就可以从原边同时传递驱动功率和信号，同步驱动所有充电管和放电管，大幅简化了脉冲电源结构和尺寸，降低了成本。以此搭建了24级固态Marx发生器，在10 kΩ阻性负载上，获得了10 kV、1 kHz、5 μs的高压方波脉冲，验证了方案的可行性，该电源主电路的尺寸仅为20 cm（长）×13 cm（宽）×5.5 cm（高）。

惯性约束聚变（ICF）中的瑞利-泰勒不稳定性（RTI）研究需要基于多种结构的调制靶，针对目前调制靶制备的工艺问题，采用双光子3D打印工艺制备了平面调制、平面复合调制及球壳型调制三种典型结构的调制靶，靶材料为光敏树脂（95%：C₂₃H₃₈N₂O₈，5%：C₄H₆O₂）。采用激光共聚焦显微成像分析了三种调制靶的实际结构参数，三种靶型的实测形貌及其参数与设计结构及参数具有良好匹配度。为进一步验证双光子3D打印新型工艺制备调制靶的可行性，实验团队在“神光Ⅱ”高功率激光实验装置上进行了纳秒激光打靶实验，结果显示靶表面的调制在激光直接驱动下受RTI的作用随时间呈增长趋势，初始峰谷值为4 μm的调制在激光驱动2.5 ns后形成了长度达100 μm的高密度射流，表明基于高精度3D打印工艺制备结构复杂的调制靶用于RTI研究具有较高可行性。